Overview

Dataset statistics

Number of variables19
Number of observations20713
Missing cells0
Missing cells (%)0.0%
Duplicate rows192
Duplicate rows (%)0.9%
Total size in memory3.0 MiB
Average record size in memory152.0 B

Variable types

Numeric12
Categorical7

Alerts

Dataset has 192 (0.9%) duplicate rowsDuplicates
latitude is highly overall correlated with zipcodeHigh correlation
livingspace is highly overall correlated with price_displayHigh correlation
longitude is highly overall correlated with zipcodeHigh correlation
number_of_rooms is highly overall correlated with object_category and 2 other fieldsHigh correlation
object_category is highly overall correlated with number_of_rooms and 2 other fieldsHigh correlation
object_type is highly overall correlated with number_of_rooms and 1 other fieldsHigh correlation
price_display is highly overall correlated with livingspace and 2 other fieldsHigh correlation
price_display_type is highly overall correlated with price_unitHigh correlation
price_unit is highly overall correlated with price_display_typeHigh correlation
year_built is highly overall correlated with year_renovatedHigh correlation
year_renovated is highly overall correlated with year_builtHigh correlation
zipcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
price_display_type is highly imbalanced (72.9%)Imbalance
price_unit is highly imbalanced (88.0%)Imbalance
is_temporary is highly imbalanced (65.2%)Imbalance
is_selling_furniture is highly imbalanced (83.8%)Imbalance
reserved is highly imbalanced (99.6%)Imbalance
livingspace is highly skewed (γ1 = 117.2959326)Skewed
object_category has 11393 (55.0%) zerosZeros
number_of_rooms has 7538 (36.4%) zerosZeros
floor has 7061 (34.1%) zerosZeros
year_built has 365 (1.8%) zerosZeros
year_renovated has 895 (4.3%) zerosZeros
livingspace has 5960 (28.8%) zerosZeros

Reproduction

Analysis started2024-07-04 08:19:52.747591
Analysis finished2024-07-04 08:20:23.268898
Duration30.52 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

object_category
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3061845
Minimum0
Maximum8
Zeros11393
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:23.438325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7410814
Coefficient of variation (CV)1.1885785
Kurtosis-0.95658547
Mean2.3061845
Median Absolute Deviation (MAD)0
Skewness0.64872885
Sum47768
Variance7.5135273
MonotonicityNot monotonic
2024-07-04T10:20:23.672174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11393
55.0%
5 3751
 
18.1%
4 3302
 
15.9%
8 1666
 
8.0%
3 331
 
1.6%
7 186
 
0.9%
2 78
 
0.4%
6 4
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
0 11393
55.0%
1 2
 
< 0.1%
2 78
 
0.4%
3 331
 
1.6%
4 3302
 
15.9%
5 3751
 
18.1%
6 4
 
< 0.1%
7 186
 
0.9%
8 1666
 
8.0%
ValueCountFrequency (%)
8 1666
 
8.0%
7 186
 
0.9%
6 4
 
< 0.1%
5 3751
 
18.1%
4 3302
 
15.9%
3 331
 
1.6%
2 78
 
0.4%
1 2
 
< 0.1%
0 11393
55.0%

object_type
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.433496
Minimum0
Maximum58
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:23.907625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median16
Q337
95-th percentile48
Maximum58
Range58
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.112954
Coefficient of variation (CV)0.93204817
Kurtosis-1.3808476
Mean19.433496
Median Absolute Deviation (MAD)14
Skewness0.39972422
Sum402526
Variance328.07912
MonotonicityNot monotonic
2024-07-04T10:20:24.160023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9318
45.0%
27 2245
 
10.8%
37 1712
 
8.3%
47 1666
 
8.0%
25 1126
 
5.4%
39 982
 
4.7%
14 610
 
2.9%
53 400
 
1.9%
16 357
 
1.7%
21 301
 
1.5%
Other values (49) 1996
 
9.6%
ValueCountFrequency (%)
0 27
 
0.1%
1 2
 
< 0.1%
2 9318
45.0%
3 12
 
0.1%
4 38
 
0.2%
5 7
 
< 0.1%
6 3
 
< 0.1%
7 225
 
1.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
58 12
 
0.1%
57 20
 
0.1%
56 4
 
< 0.1%
55 20
 
0.1%
54 127
 
0.6%
53 400
1.9%
52 1
 
< 0.1%
51 38
 
0.2%
50 241
1.2%
49 103
 
0.5%

price_display
Real number (ℝ)

HIGH CORRELATION 

Distinct2778
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1581.3384
Minimum1
Maximum62539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:24.408699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile81
Q1398
median1460
Q32141
95-th percentile3812.4
Maximum62539
Range62538
Interquartile range (IQR)1743

Descriptive statistics

Standard deviation1534.7229
Coefficient of variation (CV)0.9705215
Kurtosis145.79752
Mean1581.3384
Median Absolute Deviation (MAD)810
Skewness6.0571686
Sum32754262
Variance2355374.4
MonotonicityNot monotonic
2024-07-04T10:20:24.664994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 448
 
2.2%
130 375
 
1.8%
150 361
 
1.7%
100 255
 
1.2%
140 215
 
1.0%
50 215
 
1.0%
110 187
 
0.9%
1500 167
 
0.8%
1400 138
 
0.7%
180 136
 
0.7%
Other values (2768) 18216
87.9%
ValueCountFrequency (%)
1 6
 
< 0.1%
10 9
 
< 0.1%
15 6
 
< 0.1%
20 58
0.3%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 31
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
62539 1
< 0.1%
30000 1
< 0.1%
27155 1
< 0.1%
22665 1
< 0.1%
22000 1
< 0.1%
20500 1
< 0.1%
20000 1
< 0.1%
19390 1
< 0.1%
18493 1
< 0.1%
17520 1
< 0.1%

price_display_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
1
19751 
0
 
962

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20713
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

Length

2024-07-04T10:20:24.894449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:25.068119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

Most occurring characters

ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19751
95.4%
0 962
 
4.6%

price_unit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
1
19741 
4
 
962
0
 
5
3
 
4
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20713
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Length

2024-07-04T10:20:25.298458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:25.496226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19741
95.3%
4 962
 
4.6%
0 5
 
< 0.1%
3 4
 
< 0.1%
2 1
 
< 0.1%

number_of_rooms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9270265
Minimum0
Maximum10.5
Zeros7538
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:25.723791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33.5
95-th percentile4.5
Maximum10.5
Range10.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation1.8390604
Coefficient of variation (CV)0.95435139
Kurtosis-0.80010623
Mean1.9270265
Median Absolute Deviation (MAD)1.5
Skewness0.47071017
Sum39914.5
Variance3.3821432
MonotonicityNot monotonic
2024-07-04T10:20:26.112845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 7538
36.4%
3.5 2943
 
14.2%
1 2425
 
11.7%
4.5 2078
 
10.0%
2.5 1956
 
9.4%
3 1003
 
4.8%
2 708
 
3.4%
4 606
 
2.9%
1.5 552
 
2.7%
5.5 521
 
2.5%
Other values (10) 383
 
1.8%
ValueCountFrequency (%)
0 7538
36.4%
1 2425
 
11.7%
1.5 552
 
2.7%
2 708
 
3.4%
2.5 1956
 
9.4%
3 1003
 
4.8%
3.5 2943
 
14.2%
4 606
 
2.9%
4.5 2078
 
10.0%
5 124
 
0.6%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10 7
 
< 0.1%
9 4
 
< 0.1%
8.5 12
 
0.1%
8 13
 
0.1%
7.5 36
 
0.2%
7 24
 
0.1%
6.5 106
 
0.5%
6 56
 
0.3%
5.5 521
2.5%

floor
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2988461
Minimum-8
Maximum31
Zeros7061
Zeros (%)34.1%
Negative2171
Negative (%)10.5%
Memory size161.9 KiB
2024-07-04T10:20:26.476007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-1
Q10
median1
Q32
95-th percentile5
Maximum31
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1092934
Coefficient of variation (CV)1.6239748
Kurtosis14.86672
Mean1.2988461
Median Absolute Deviation (MAD)1
Skewness2.4330329
Sum26903
Variance4.4491185
MonotonicityNot monotonic
2024-07-04T10:20:26.777188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 7061
34.1%
1 3607
17.4%
2 3294
15.9%
3 2249
 
10.9%
-1 1764
 
8.5%
4 1134
 
5.5%
5 565
 
2.7%
-2 291
 
1.4%
6 254
 
1.2%
7 112
 
0.5%
Other values (23) 382
 
1.8%
ValueCountFrequency (%)
-8 1
 
< 0.1%
-6 2
 
< 0.1%
-5 6
 
< 0.1%
-4 34
 
0.2%
-3 73
 
0.4%
-2 291
 
1.4%
-1 1764
 
8.5%
0 7061
34.1%
1 3607
17.4%
2 3294
15.9%
ValueCountFrequency (%)
31 1
 
< 0.1%
25 1
 
< 0.1%
24 3
 
< 0.1%
23 3
 
< 0.1%
22 2
 
< 0.1%
21 2
 
< 0.1%
19 3
 
< 0.1%
18 7
< 0.1%
17 4
< 0.1%
16 8
< 0.1%

is_furnished
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
0.0
17750 
1.0
2963 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters62139
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17750
85.7%
1.0 2963
 
14.3%

Length

2024-07-04T10:20:27.060264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:27.636575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17750
85.7%
1.0 2963
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 38463
61.9%
. 20713
33.3%
1 2963
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38463
61.9%
. 20713
33.3%
1 2963
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38463
61.9%
. 20713
33.3%
1 2963
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38463
61.9%
. 20713
33.3%
1 2963
 
4.8%

is_temporary
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
0.0
19361 
1.0
 
1352

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters62139
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19361
93.5%
1.0 1352
 
6.5%

Length

2024-07-04T10:20:27.825569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:28.000631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19361
93.5%
1.0 1352
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 40074
64.5%
. 20713
33.3%
1 1352
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40074
64.5%
. 20713
33.3%
1 1352
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40074
64.5%
. 20713
33.3%
1 1352
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40074
64.5%
. 20713
33.3%
1 1352
 
2.2%

is_selling_furniture
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
0.0
20220 
1.0
 
493

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters62139
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 20220
97.6%
1.0 493
 
2.4%

Length

2024-07-04T10:20:28.184196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:28.360949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 20220
97.6%
1.0 493
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 40933
65.9%
. 20713
33.3%
1 493
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 40933
65.9%
. 20713
33.3%
1 493
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 40933
65.9%
. 20713
33.3%
1 493
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 40933
65.9%
. 20713
33.3%
1 493
 
0.8%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct1744
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5563.7576
Minimum1000
Maximum9657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:28.577809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1146
Q13097
median5430
Q38152
95-th percentile9016
Maximum9657
Range8657
Interquartile range (IQR)5055

Descriptive statistics

Standard deviation2743.8318
Coefficient of variation (CV)0.49316163
Kurtosis-1.3984882
Mean5563.7576
Median Absolute Deviation (MAD)2620
Skewness-0.16886561
Sum1.1524211 × 108
Variance7528612.8
MonotonicityNot monotonic
2024-07-04T10:20:28.831310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 432
 
2.1%
1700 313
 
1.5%
8050 277
 
1.3%
8004 244
 
1.2%
2300 192
 
0.9%
8048 179
 
0.9%
4052 179
 
0.9%
8400 175
 
0.8%
4123 159
 
0.8%
8003 154
 
0.7%
Other values (1734) 18409
88.9%
ValueCountFrequency (%)
1000 6
 
< 0.1%
1001 1
 
< 0.1%
1002 2
 
< 0.1%
1003 86
0.4%
1004 84
0.4%
1005 31
 
0.1%
1006 45
0.2%
1007 70
0.3%
1008 50
0.2%
1009 43
0.2%
ValueCountFrequency (%)
9657 3
 
< 0.1%
9656 4
 
< 0.1%
9650 5
 
< 0.1%
9643 2
 
< 0.1%
9642 1
 
< 0.1%
9630 18
0.1%
9620 5
 
< 0.1%
9615 2
 
< 0.1%
9607 2
 
< 0.1%
9606 11
0.1%

city
Real number (ℝ)

Distinct2158
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1120.006
Minimum0
Maximum2157
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:29.082521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile127
Q1500
median1101
Q31732
95-th percentile2065
Maximum2157
Range2157
Interquartile range (IQR)1232

Descriptive statistics

Standard deviation683.64812
Coefficient of variation (CV)0.61039684
Kurtosis-1.3496431
Mean1120.006
Median Absolute Deviation (MAD)608
Skewness-0.059066146
Sum23198684
Variance467374.75
MonotonicityNot monotonic
2024-07-04T10:20:29.331863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2065 1969
 
9.5%
146 956
 
4.6%
1678 630
 
3.0%
178 543
 
2.6%
2054 470
 
2.3%
976 454
 
2.2%
1992 354
 
1.7%
644 312
 
1.5%
675 268
 
1.3%
1055 203
 
1.0%
Other values (2148) 14554
70.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 7
 
< 0.1%
5 93
0.4%
6 19
 
0.1%
7 4
 
< 0.1%
8 27
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2157 1
 
< 0.1%
2156 1
 
< 0.1%
2155 11
0.1%
2154 16
0.1%
2153 1
 
< 0.1%
2152 1
 
< 0.1%
2151 1
 
< 0.1%
2150 1
 
< 0.1%
2149 1
 
< 0.1%
2148 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct16148
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.159021
Minimum45.826182
Maximum47.793652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:29.571475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45.826182
5-th percentile46.213842
Q146.956562
median47.346442
Q347.430642
95-th percentile47.560042
Maximum47.793652
Range1.96747
Interquartile range (IQR)0.47408001

Descriptive statistics

Standard deviation0.40426707
Coefficient of variation (CV)0.008572423
Kurtosis0.67415433
Mean47.159021
Median Absolute Deviation (MAD)0.16956
Skewness-1.2398413
Sum976804.8
Variance0.16343187
MonotonicityNot monotonic
2024-07-04T10:20:29.840028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.77822161 27
 
0.1%
46.0127616 23
 
0.1%
47.43064161 22
 
0.1%
47.4241816 21
 
0.1%
47.1131916 20
 
0.1%
46.23478161 18
 
0.1%
47.11767161 15
 
0.1%
47.21403161 15
 
0.1%
47.44245161 14
 
0.1%
47.1582316 14
 
0.1%
Other values (16138) 20524
99.1%
ValueCountFrequency (%)
45.8261816 1
 
< 0.1%
45.8310216 1
 
< 0.1%
45.8322416 3
< 0.1%
45.8329516 2
< 0.1%
45.8332216 1
 
< 0.1%
45.83389161 2
< 0.1%
45.8353816 1
 
< 0.1%
45.8357516 1
 
< 0.1%
45.8379816 1
 
< 0.1%
45.83800161 1
 
< 0.1%
ValueCountFrequency (%)
47.79365161 1
< 0.1%
47.7680316 1
< 0.1%
47.7566216 1
< 0.1%
47.75052161 1
< 0.1%
47.75009161 1
< 0.1%
47.75003161 1
< 0.1%
47.7495116 1
< 0.1%
47.7469616 1
< 0.1%
47.74692161 1
< 0.1%
47.74690161 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct16486
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0244014
Minimum5.9918812
Maximum10.364311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:30.108092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.9918812
5-th percentile6.5684812
Q17.4677812
median8.2070612
Q38.5875712
95-th percentile9.3810212
Maximum10.364311
Range4.37243
Interquartile range (IQR)1.11979

Descriptive statistics

Standard deviation0.84775823
Coefficient of variation (CV)0.10564754
Kurtosis-0.62952085
Mean8.0244014
Median Absolute Deviation (MAD)0.61179
Skewness-0.28703371
Sum166209.43
Variance0.71869401
MonotonicityNot monotonic
2024-07-04T10:20:30.355298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.156761234 27
 
0.1%
8.96301124 23
 
0.1%
6.840561235 23
 
0.1%
9.383271235 22
 
0.1%
8.558061235 21
 
0.1%
7.301061235 20
 
0.1%
6.124081239 18
 
0.1%
7.785771235 15
 
0.1%
8.521711235 13
 
0.1%
8.965131235 13
 
0.1%
Other values (16476) 20518
99.1%
ValueCountFrequency (%)
5.991881235 1
< 0.1%
5.993681235 1
< 0.1%
6.019591235 1
< 0.1%
6.019631235 1
< 0.1%
6.036691235 1
< 0.1%
6.049101235 1
< 0.1%
6.061721235 1
< 0.1%
6.062851235 1
< 0.1%
6.066581235 1
< 0.1%
6.067841235 1
< 0.1%
ValueCountFrequency (%)
10.36431123 1
< 0.1%
10.29604123 1
< 0.1%
10.09659123 1
< 0.1%
9.880551235 1
< 0.1%
9.875381235 1
< 0.1%
9.868861235 1
< 0.1%
9.833921236 1
< 0.1%
9.831621235 1
< 0.1%
9.824991235 1
< 0.1%
9.824821235 1
< 0.1%

year_built
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct179
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.998547
Minimum-2
Maximum224
Zeros365
Zeros (%)1.8%
Negative41
Negative (%)0.2%
Memory size161.9 KiB
2024-07-04T10:20:30.599908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile4
Q138.946387
median38.946387
Q338.946387
95-th percentile69
Maximum224
Range226
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.681407
Coefficient of variation (CV)0.55595423
Kurtosis14.297883
Mean38.998547
Median Absolute Deviation (MAD)0
Skewness2.4436148
Sum807776.91
Variance470.08343
MonotonicityNot monotonic
2024-07-04T10:20:30.849106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.94638748 12536
60.5%
0 365
 
1.8%
1 239
 
1.2%
2 188
 
0.9%
6 184
 
0.9%
7 176
 
0.8%
54 167
 
0.8%
5 163
 
0.8%
9 163
 
0.8%
8 160
 
0.8%
Other values (169) 6372
30.8%
ValueCountFrequency (%)
-2 9
 
< 0.1%
-1 32
 
0.2%
0 365
1.8%
1 239
1.2%
2 188
0.9%
3 157
0.8%
4 143
 
0.7%
5 163
0.8%
6 184
0.9%
7 176
0.8%
ValueCountFrequency (%)
224 18
0.1%
219 1
 
< 0.1%
217 1
 
< 0.1%
216 2
 
< 0.1%
213 2
 
< 0.1%
212 2
 
< 0.1%
209 2
 
< 0.1%
206 1
 
< 0.1%
204 2
 
< 0.1%
199 1
 
< 0.1%

year_renovated
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.567108
Minimum-3
Maximum224
Zeros895
Zeros (%)4.3%
Negative50
Negative (%)0.2%
Memory size161.9 KiB
2024-07-04T10:20:31.092397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile1
Q115
median38.946387
Q338.946387
95-th percentile55
Maximum224
Range227
Interquartile range (IQR)23.946387

Descriptive statistics

Standard deviation19.387146
Coefficient of variation (CV)0.61415653
Kurtosis7.3682444
Mean31.567108
Median Absolute Deviation (MAD)0
Skewness1.058426
Sum653849.51
Variance375.86141
MonotonicityNot monotonic
2024-07-04T10:20:31.349605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.94638748 11443
55.2%
0 895
 
4.3%
1 600
 
2.9%
2 431
 
2.1%
3 341
 
1.6%
4 332
 
1.6%
7 325
 
1.6%
6 324
 
1.6%
5 312
 
1.5%
9 277
 
1.3%
Other values (142) 5433
26.2%
ValueCountFrequency (%)
-3 1
 
< 0.1%
-2 9
 
< 0.1%
-1 40
 
0.2%
0 895
4.3%
1 600
2.9%
2 431
2.1%
3 341
 
1.6%
4 332
 
1.6%
5 312
 
1.5%
6 324
 
1.6%
ValueCountFrequency (%)
224 5
< 0.1%
188 2
 
< 0.1%
187 1
 
< 0.1%
178 2
 
< 0.1%
174 3
< 0.1%
172 1
 
< 0.1%
170 1
 
< 0.1%
166 1
 
< 0.1%
163 3
< 0.1%
162 2
 
< 0.1%

moving_date_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
1
7313 
2
7248 
0
6152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20713
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

Length

2024-07-04T10:20:31.573605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:31.766212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

Most occurring characters

ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7313
35.3%
2 7248
35.0%
0 6152
29.7%

reserved
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.9 KiB
0.0
20706 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters62139
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 20706
> 99.9%
1.0 7
 
< 0.1%

Length

2024-07-04T10:20:31.962760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T10:20:32.138496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 20706
> 99.9%
1.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 41419
66.7%
. 20713
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41419
66.7%
. 20713
33.3%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41419
66.7%
. 20713
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41419
66.7%
. 20713
33.3%
1 7
 
< 0.1%

livingspace
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct605
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.059962
Minimum0
Maximum90000
Zeros5960
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size161.9 KiB
2024-07-04T10:20:32.351879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58
Q394
95-th percentile196
Maximum90000
Range90000
Interquartile range (IQR)94

Descriptive statistics

Standard deviation673.65857
Coefficient of variation (CV)8.3106203
Kurtosis15398.293
Mean81.059962
Median Absolute Deviation (MAD)46
Skewness117.29593
Sum1678995
Variance453815.87
MonotonicityNot monotonic
2024-07-04T10:20:32.594025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5960
28.8%
70 370
 
1.8%
80 336
 
1.6%
100 320
 
1.5%
90 285
 
1.4%
75 277
 
1.3%
60 262
 
1.3%
65 255
 
1.2%
85 237
 
1.1%
12 237
 
1.1%
Other values (595) 12174
58.8%
ValueCountFrequency (%)
0 5960
28.8%
1 16
 
0.1%
2 27
 
0.1%
3 10
 
< 0.1%
4 8
 
< 0.1%
5 4
 
< 0.1%
6 10
 
< 0.1%
7 14
 
0.1%
8 27
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
23000 1
< 0.1%
11000 1
< 0.1%
8033 1
< 0.1%
8000 1
< 0.1%
6000 1
< 0.1%
5443 2
< 0.1%
3934 1
< 0.1%
3892 1
< 0.1%
3773 1
< 0.1%

Interactions

2024-07-04T10:20:20.217659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:55.215627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:57.479773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:59.686488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:02.300799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.562159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:06.850576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.965829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:11.088317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:13.434446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.918072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:18.068228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:20.410151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:55.422363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:57.680010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:59.886656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:02.494704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.774850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.043259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:09.159409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:11.295645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:13.621481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:16.113728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:18.261925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:20.589690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:55.619679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:57.866513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:00.075375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:02.674188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.960294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.220860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:09.336874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:11.492565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:14.195580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:16.292712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:18.449147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:20.766632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:55.817361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.056900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:00.611247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:02.870316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:05.154786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.406510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:09.522601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:11.696058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:14.370811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:16.476312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:18.639981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:20.943175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:55.998395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.237879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:00.790860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:03.050199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:05.338042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.582256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:09.705933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:11.885902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:14.548263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:16.655210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:18.822315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.136730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:56.193323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.432113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:00.980819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:03.249547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:05.536043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.765937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:09.894126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:12.093264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:14.729806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:16.846018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.007024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.303740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:56.376296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.608670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:01.149851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:03.450322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:05.721107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:07.932174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.060374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:12.280804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:14.891936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.020708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.179373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.464359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:56.562368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.786242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:01.324458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:03.635868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:05.906442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.096927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.222778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:12.469011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.054632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.189179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.345025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.664647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:56.773626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:58.992249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:01.554316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:03.845609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:06.117175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.299999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.424877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:12.691264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.250602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.392806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.545399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.837277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:56.948980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:59.165837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:01.761669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.014515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:06.295062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.466277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.585842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:12.885228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.420368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.560406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.715775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:21.997609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:57.122991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:59.339870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:01.942765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.185034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:06.476171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.630743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.752887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:13.065918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.586809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.729760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:19.877129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:22.163446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:57.303627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:19:59.514364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:02.124152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:04.379973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:06.668237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:08.794961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:10.921813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:13.252449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:15.753217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:17.897906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T10:20:20.043085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-07-04T10:20:32.784982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
cityflooris_furnishedis_selling_furnitureis_temporarylatitudelivingspacelongitudemoving_date_typenumber_of_roomsobject_categoryobject_typeprice_displayprice_display_typeprice_unitreservedyear_builtyear_renovatedzipcode
city1.000-0.0130.1910.0620.1710.045-0.0590.2980.059-0.0470.0680.0580.0370.0250.0100.000-0.014-0.0150.308
floor-0.0131.0000.1290.0480.0700.0040.337-0.0270.1310.469-0.460-0.3410.4090.0230.0000.0000.032-0.034-0.041
is_furnished0.1910.1291.0000.0280.4810.025-0.0390.1140.1760.0550.0010.1250.1580.0810.0830.000-0.0110.0240.102
is_selling_furniture0.0620.0480.0281.0000.0060.0080.0050.0350.0700.037-0.011-0.0330.0350.0140.0440.000-0.029-0.0340.035
is_temporary0.1710.0700.4810.0061.0000.0380.0140.0770.1570.0610.0070.0360.0700.0270.0280.000-0.0070.0390.084
latitude0.0450.0040.0250.0080.0381.000-0.0100.4560.078-0.0160.0420.015-0.0430.0240.0170.0000.001-0.0550.537
livingspace-0.0590.337-0.0390.0050.014-0.0101.000-0.0290.0050.458-0.423-0.2320.6040.0530.0280.000-0.073-0.173-0.038
longitude0.298-0.0270.1140.0350.0770.456-0.0291.0000.079-0.0330.0760.070-0.0370.0340.0150.000-0.043-0.0950.944
moving_date_type0.0590.1310.1760.0700.1570.0780.0050.0791.000-0.0370.0780.018-0.1360.1220.0860.0170.0130.049-0.031
number_of_rooms-0.0470.4690.0550.0370.061-0.0160.458-0.033-0.0371.000-0.747-0.6310.6360.2270.1130.007-0.012-0.147-0.041
object_category0.068-0.4600.001-0.0110.0070.042-0.4230.0760.078-0.7471.0000.795-0.6530.4960.2490.000-0.0030.1160.099
object_type0.058-0.3410.125-0.0330.0360.015-0.2320.0700.018-0.6310.7951.000-0.4080.3450.1750.000-0.0060.1060.083
price_display0.0370.4090.1580.0350.070-0.0430.604-0.037-0.1360.636-0.653-0.4081.0000.0180.0000.000-0.074-0.150-0.044
price_display_type0.0250.0230.0810.0140.0270.0240.0530.0340.1220.2270.4960.3450.0181.0001.0000.0000.0340.024-0.022
price_unit0.0100.0000.0830.0440.0280.0170.0280.0150.0860.1130.2490.1750.0001.0001.0000.000-0.035-0.0250.022
reserved0.0000.0000.0000.0000.0000.0000.0000.0000.0170.0070.0000.0000.0000.0000.0001.000-0.014-0.025-0.005
year_built-0.0140.032-0.011-0.029-0.0070.001-0.073-0.0430.013-0.012-0.003-0.006-0.0740.034-0.035-0.0141.0000.516-0.053
year_renovated-0.015-0.0340.024-0.0340.039-0.055-0.173-0.0950.049-0.1470.1160.106-0.1500.024-0.025-0.0250.5161.000-0.096
zipcode0.308-0.0410.1020.0350.0840.537-0.0380.944-0.031-0.0410.0990.083-0.044-0.0220.022-0.005-0.053-0.0961.000

Missing values

2024-07-04T10:20:22.425108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-04T10:20:23.013311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
0527130.0110.00.00.00.00.05600100447.3847538.18275038.94638738.94638700.00.0
1442325.0110.00.00.00.00.0860050047.3978868.6008506.0000006.00000020.0119.0
2051610.0111.00.01.00.00.09008167847.4424309.39251038.94638738.94638710.017.0
38471350.0111.05.01.01.00.08005206547.3893038.51320020.00000020.00000010.018.0
4022370.0112.50.00.00.00.02000119046.7988036.85501020.00000020.00000020.0145.0
5441110.0110.00.00.00.00.0828091347.6519839.17038738.94638738.94638720.00.0
653940.0110.00.00.00.00.04208123647.3935007.61456238.94638738.94638720.00.0
744195.0110.00.00.00.00.04632178747.3701607.91466638.94638738.94638720.00.0
852795.0110.00.00.00.00.0422625147.4022917.54437938.94638738.94638720.00.0
953960.0110.00.00.00.00.0501856547.3979018.01652938.94638738.94638700.00.0
object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace
207038471100.0111.02.00.01.00.08055206547.3718928.51135138.94638738.94638710.00.0
20704527130.0110.00.00.00.00.08604189547.4061228.68111138.94638738.94638720.00.0
207058471137.0111.01.00.00.00.08004206547.3808028.517321174.00000014.00000020.0105.0
20706023270.0113.52.01.00.00.08048205447.3848728.4936516.0000006.00000010.0102.0
207078471390.0111.00.00.00.00.08002206547.3644728.53362138.94638738.94638720.00.0
207088471290.0111.01.01.00.00.08006206547.3899728.53759194.0000005.00000020.016.0
20709022500.0114.01.00.00.00.0830290547.4448728.58114164.0000001.00000010.074.0
20710847600.0111.00.01.00.00.0250320147.1289427.26054182.00000016.00000000.060.0
20711021125.0111.01.00.00.00.0306523346.9736627.4893310.0000000.00000000.027.0
20712022050.0113.52.00.00.00.0895347847.4058128.40345138.94638738.94638710.092.0

Duplicate rows

Most frequently occurring

object_categoryobject_typeprice_displayprice_display_typeprice_unitnumber_of_roomsflooris_furnishedis_temporaryis_selling_furniturezipcodecitylatitudelongitudeyear_builtyear_renovatedmoving_date_typereservedlivingspace# duplicates
137527250.0110.0-2.00.00.00.0121898146.2347826.12408112.00000012.00000020.00.014
84527100.0110.0-1.00.00.00.08718159947.1582329.07289135.00000035.00000020.01.07
133527230.0110.0-2.00.00.00.0120367546.2050326.12795119.00000019.00000020.00.07
7451640.0110.00.00.00.00.0102236646.5330626.58047159.00000017.00000020.00.06
7551640.0110.00.00.00.00.0102236646.5337326.58070163.00000021.00000020.00.06
85527100.0110.0-1.00.00.00.08718159947.1582329.07289135.00000035.00000020.014.06
15253985.0110.00.00.00.00.01400202746.7685626.64147151.00000014.00000020.011.05
160539110.0110.00.00.00.00.0230093247.0914426.81228138.94638738.94638720.00.05
138527250.0110.0-1.00.00.00.0121898146.2347826.12408112.00000012.00000020.00.04
14553950.0110.00.00.00.00.0206878047.0164226.97437138.94638738.94638720.00.04